Given a target dataset \(\mathcal {D}_t = (\mathcal {X}_t,\mathcal {Y}_t)\)  where \(\mathcal {X}_t = \lbrace x_t^1, x_t^2, x_t^3,...,x_t^M \rbrace \)  with their ID labels \(\mathcal {Y}_t\) . We target to select a subset \(\mathcal {D}_{s}^{\prime }\)  from a large-scale source/pre-training dataset \(\mathcal {D}_{s}\)  where \(\mathcal {X}_s = \lbrace x_s^1, x_s^2, x_s^3,...,x_s^N \rbrace \) .
The number of images in \(\mathcal {D}_{s}^{\prime }\)  is \(N^{\prime } < N\) . The efficient conditional pre-training is to pre-train models on \(\mathcal {D}_{s}^{\prime }\) , which should reduce the training cost of pre-training while maintaining or even improving performance on the target dataset. Some previous works [1]}, [2]}, [3]} have shown that the solution is to select pre-training data close to the target domain, and we also provide theoretical analysis in Appendix to further verify this. Since ReID is a open set problem with different IDs in training and testing set, the key problem is how to design a metric to evaluate the `similarity' between the pre-training data \(x_s^i, i \in [1,N]\)  and the target dataset \(\mathcal {D}_{t}\)  (instead of the person IDs in \(\mathcal {D}_{t}\) ).
